A Seven-Gene Signature with Close Immune Correlation Was Identified for Survival Prediction of Lung Adenocarcinoma

The bioinformatics analysis and experimental validation of the carcinogenic role of EXO1 in lung adenocarcinoma

Background

Exonuclease 1 (EXO1), a protein involved in mismatch repair and recombination processes, has been identified as a prognostic biomarker in lung adenocarcinoma (LUAD). Nevertheless, its role in LUAD progression remains elusive. This study seeks to elucidate the functional significance of EXO1 in LUAD and evaluate its potential as a therapeutic target.

Materials and methods

Patient RNA-seq and clinical data were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Subsequently, a protein-protein interaction (PPI) network was constructed using differentially expressed genes (DEGs) to identify pivotal genes. Validation of the expression of signature genes was carried out through quantitative real-time PCR (qRT-PCR). Additionally, the association between EXO1 expression and clinical data was investigated. Immunohistochemistry was utilized to assess EXO1 expression in 93 cases of invasive pulmonary adenocarcinoma. Finally, cellular functional assays were conducted to investigate the impact of EXO1 on LUAD cells.

Results

Ten key molecules (PBK, ASPM, NCAPG, EXO1, MKI67, RRM2, AURKA, DLGAP5, UBE2C, and CDC6) exhibited significantly elevated expression levels in LUAD tissues. Moreover, elevated levels of EXO1 gene expression correlated strongly with advanced T, N, and M stages and were significantly associated with immune cell infiltration in LUAD. Furthermore, marked increases in EXO1 protein expression were observed in patients diagnosed with invasive pulmonary adenocarcinoma. Notably, patients diagnosed with invasive pulmonary adenocarcinoma who exhibited elevated EXO1 expression levels exhibited increased lymph node metastasis, pleural invasion, poor tumor differentiation, and advanced clinical stage. Additionally, this study employed wound healing assay and CCK-8 cell proliferation assays to investigate the significant role of EXO1 in promoting the growth and migration of lung adenocarcinoma cells.

Conclusions

This study identified ten hub genes associated with the initiation and progression of LUAD. Additionally, EXO1 may serve as a prognostic marker for LUAD patients, offering new perspectives for clinical treatments.

Establishment of a Prognostic Model of Lung Adenocarcinoma Based on Tumor Heterogeneity

Lung cancer is one of the main cancer types due to its persistently high incidence and mortality, yet a simple and effective prognostic model is still lacking. This study aimed to identify independent prognostic genes related to the heterogeneity of lung adenocarcinoma (LUAD), generate a prognostic risk score model, and construct a nomogram in combination with other pathological characteristics to predict patients’ overall survival (OS). A significant amount of data pertaining to single-cell RNA sequencing (scRNA-seq), RNA sequencing (RNA-seq), and somatic mutation were used for data mining. After statistical analyses, a risk scoring model was established based on eight independent prognostic genes, and the OS of high-risk patients was significantly lower than that of low-risk patients. Interestingly, high-risk patients were more sensitive and effective to immune checkpoint blocking therapy. In addition, it was noteworthy that CCL20 not only affected prognosis and differentiation of LUAD but also led to poor histologic grade of tumor cells. Ultimately, combining risk score, clinicopathological information, and CCL20 mutation status, a nomogram with good predictive performance and high accuracy was established. In short, our research established a prognostic model that could be used to guide clinical practice based on the constantly updated big multi-omics data. Finally, this analysis revealed that CCL20 may become a potential therapeutic target for LUAD.

A narrative review of prognosis prediction models for non-small cell lung cancer: what kind of predictors should be selected and how to improve models?

Objective

To discover potential predictors and explore how to build better models by summarizing the existing prognostic prediction models of non-small cell lung cancer (NSCLC).

Background

Research on clinical prediction models of NSCLC has experienced explosive growth in recent years. As more predictors of prognosis are discovered, the choice of predictors to build models is particularly important, and in the background of more applications of next-generation sequencing technology, gene-related predictors are widely used. As it is more convenient to obtain samples and follow-up data, the prognostic model is preferred by researchers.

Methods

PubMed and the Cochrane Library were searched using the items “NSCLC”, “prognostic model”, “prognosis prediction”, and “survival prediction” from 1 January 1980 to 5 May 2021. Reference lists from articles were reviewed and relevant articles were identified.

Conclusions

The performance of gene-related models has not obviously improved. Relative to the innovation and diversity of predictors, it is more important to establish a highly stable model that is convenient for clinical application. Most of the prevalent models are highly biased and referring to PROBAST at the beginning of the study may be able to significantly control the bias. Existing models should be validated in a large external dataset to make a meaningful comparison.